| Sumario: | Summary
This report presents the findings from the validation trials conducted on rice fertilizer recommendations in Rwanda under the collaborative efforts of the Rwanda Soil Information System (RwaSIS) and the Excellence in Agronomy Initiative (EiA). The initiative aimed to enhance agricultural productivity by delivering site-specific fertilizer recommendations (SSR) as opposed to blanket recommendations (BR) traditionally used across Rwanda. The introduction of SSR was facilitated by digital tools and the public-private partnership extension model between the CGIAR and the Rwanda Agriculture and Animal Resources Board (RAB).
Key Findings
Yield Improvement: The trials demonstrated that SSR consistently outperformed both BR and improved blanket recommendations (IBR). SSR led to significantly higher yields across different agroecological zones (AEZs), with potential yield gains of 20% or more for a large portion of farmers. For instance, in the Central Plateau and Mayaga regions, SSR showed a 90% yield increase for participating farmers compared to BR. However, regions like Bugesera displayed a unique low response to SSR, underscoring the need for region-specific interventions.
Economic Benefits: The economic analysis underscored the cost-effectiveness and profitability of SSR. Farmers using SSR experienced improved profitability due to increased yields and more efficient fertilizer use. Around 84% of farmers recorded higher profits with SSR compared to BR, with the potential to improve the financial sustainability of fertilizer investments.
Agroecological Variability: Results varied significantly across different AEZs, with factors such as local environmental conditions and rice varieties playing a crucial role in determining the efficacy of fertilizer recommendations. The Eastern Plateau, for instance, saw moderate success, while the Bugesera region faced challenges, highlighting the necessity for adaptive strategies.
Technology Integration: The successful application of digital tools, including the Rwanda Soil Information System (RwaSIS), facilitated the efficient gathering, analysis, and dissemination of data. These tools supported real-time decision-making and the development of SSR tailored to the specific needs of Rwanda's diverse agroecological zones. The integration of these technologies holds promise for further optimizing agricultural productivity through precision agriculture. The superiority of SSR is supported by rigorous statistical analyses and post hoc tests, which confirmed significant differences in yields between SSR and both BR and IBR. The data was collected from over 300 on-farm trials and analyzed using the advanced AGWISE platform. The success of SSR reflects the careful consideration of local conditions and crop-specific requirements, proving that site-specific interventions lead to more effective resource use and higher productivity.
Furthermore, the economic analysis provides strong justification for scaling up the use of SSR. By promoting efficient fertilizer use, SSR not only boosts yield but also mitigates environmental risks and enhances the return on investment. This data-driven approach ensures that the findings are robust, reliable, and actionable, laying the foundation for informed decision-making by policymakers and stakeholders.
Conclusion
The findings from the validation trials provide compelling evidence in favor of adopting SSR over traditional BR methods. The report demonstrates that SSR offers significant benefits in terms of both yield improvement and economic profitability, especially when adapted to local agroecological conditions. Policymakers are encouraged to scale up SSR-based recommendations to further enhance Rwanda's agricultural productivity, food security, and economic development. Targeted interventions are recommended for regions like Bugesera, where distinct agroecological dynamics hinder the full potential of SSR. Additionally, investment in capacity building
and digital infrastructure is crucial for empowering farmers with the tools and knowledge needed to maximize the benefits of precision agriculture.
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